cytopy.flow.cell_classifier.keras_classifier¶

This module contains the base class KerasCellClassifier for using deep learning methods, trained on some labeled FileGroup (has existing Populations), to predict single cell classifications.

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Classes:

KerasCellClassifier(model, optimizer, loss, …)

Use Keras deep learning models to predict the classification of single cell data.

class cytopy.flow.cell_classifier.keras_classifier.KerasCellClassifier(model: Optional[tensorflow.python.keras.engine.sequential.Sequential] = None, optimizer: Optional[str] = None, loss: Optional[str] = None, metrics: Optional[list] = None, **kwargs)¶

Use Keras deep learning models to predict the classification of single cell data. Training data should be provided in the form of a FileGroup with existing Populations. Supports multi-class and multi-label classification; if multi-label classification is chosen, the tree structure of training data is NOT conserved - all resulting populations will have the same parent population.

Note, this class assumes you use the Keras Sequential API. Objects can be constructed using a pre-built model, or the model designed through the parameters ‘optimizer’, ‘loss’ and ‘metrics, and then a model constructed using the ‘build_model’ method.

Parameters

model (Sequential, optional) – Pre-compiled Keras Sequential model
optimizer (str, optional) – Provide if you intend to compile a model with the ‘build_model’ method. See https://keras.io/api/optimizers/ for optimizers
loss (str, optional) – Provide if you intend to compile a model with the ‘build_model’ method. See https://keras.io/api/losses/ for valid loss functions
metrics (list, optional) – Provide if you intend to compile a model with the ‘build_model’ method. See https://keras.io/api/metrics/ for valid metrics
features (list) – List of channels/markers to use as features in prediction
target_populations (list) – List of populations from training data to predict
multi_label (bool (default=False)) – If True, single cells can belong to more than one population. The tree structure of training data is NOT conserved - all resulting populations will have the same parent population.
logging_level (int (default=logging.INFO)) – Level to log events at
log (str, optional) – Path to log output to; if not given, will log to stdout
population_prefix (str (default=”CellClassifier_”)) – Prefix applied to populations generated

scaler¶

Scaler object

Type: Scaler

transformer¶

Transformer object

Type: Transformer

class_weights¶

Sample class weights; key is sample index, value is weight. Set by calling compute_class_weights.

Type: dict

x¶

Training feature space

Type: Pandas.DataFrame

y¶

Target labels

Type: numpy.ndarray

logger¶

Type: logging.Logger

features¶

Type: list

target_populations¶

Type: list

Methods:

build_model(layers, layer_params[, input_shape])

If Sequential model is not constructed and provided at object construction, this method can be used to specify a sequential model to be built.

build_model(layers: list, layer_params: list, input_shape: Optional[tuple] = None, **compile_kwargs)¶

If Sequential model is not constructed and provided at object construction, this method can be used to specify a sequential model to be built.

Parameters

layers (list) – List of keras layer class names (see https://keras.io/api/layers/)
layer_params (list) – List of parameters to use when constructing layers (order must match layers)
input_shape (tuple, optional) – Shape of input data to first layer, if None, then passed as (N, ) where N is the number of features
compile_kwargs – Additional keyword arguments passed when calling compile

Returns

Return type

self